Go to homepage
Find educational resourcesKnowledge & expertiseCommunities
NL|EN
Privacy policyParticipating institutions
Creative CommonsCreative Commons AttributionSURFFunded by the European Union

Filter

Search results (27)

HBO/WO

MBO

July 21, 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

August 23, 2023

Lecture: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

Website

HCAIM Consortium

August 23, 2023

Lecture: Hyperparameter tuning

Investigate effects on capacity and depth Experient with varying epochs and batch sizes Trial different activation functions and learning rates

Website

HCAIM Consortium

September 17, 2024

Dynamic data processing: Recursive least-squares

This book is a follow-up on Adjustment theory. It extends the theory to the case of time-varying parameters with an emphasis on their recursive determination. Least-squares estimation will be the leading principle used. A least-squares solution is said to be recursive when the method of computation enables sequential, rather than batch, processing of the measurement data. The recursive equations enable the updating of parameter estimates for new observations without the need to store all past observations. Methods of recursive least-squares estimation are therefore particularly useful for applications in which the time-varying parameters need to be instantly determined. Important examples of such applications can be found in the fields of real-time kinematic positioning, navigation and guidance, or multivariate time series analysis. The goal of this book is therefore to convey the necessary knowledge to be able to process sequentially collected measurements for the purpose of estimating time-varying parameters. When determining time-varying parameters from sequentially collected measurement data, one can discriminate between three types of estimation problems: filtering, prediction and smoothing. Filtering aims at the determination of current parameter values, while smoothing and prediction aim at the determination of respectively past and future parameter values. The emphasis in this book will be on recursive least-squares filtering. The theory is worked out for the important case of linear(ized) models. The measurement-update and time-update equations of recursive least-squares are discussed in detail. Models with sequentially collected data, but time-invariant parameters are treated first. In this case only the measurement-update equations apply. State-space models for dynamic systems are discussed so as to include time-varying parameters. This includes their linearization and the construction of the state transition matrix. Elements from the theory of random functions are used to describe the propagation laws for linear dynamic systems. The theory is illustrated by means of many worked out examples. They are drawn from applications such as kinematic positioning, satellite orbit determination and inertial navigation.

Open (Access) Text Book

Peter Teunissen

April 22, 2024

From theORy to application: learning to optimize with Operations Research in an interactive way

This book serves as a comprehensive roadmap for navigating the realm of Operations Research (OR). From laying down fundamental mathematical principles to crafting precise modeling techniques and their solution methods, it culminates in a panoramic view of OR models mirroring real-world operations. Delving into diverse applications-from assignment problems to network problems like graph coloring and minimum spanning trees, and navigating through routing problems that are very common in logistics-the book equips readers with practical insights. Each model is accompanied by meticulously detailed examples, seamlessly integrated with hyperlinked codes accessible via an open repository. Moreover, it introduces an engaging dimension with hyperlinks to three serious games replicating some cornerstone OR models, offering a playful yet educational environment for solo or group experimentation.

Open (Access) Text Book

Alessandro Bombelli, Bilge Atasoy, Stefano Fazi, Doris Boschma

July 1, 2025

AI as a science - course manual

AI as a Science aims to highlight how research involving “artificial intelligence” can be considered scientific, and what that means for us as scholars engaged in this — and related — areas of study. During the course, we will explore answers to questions such as: What is AI? What role do scientists play in the development of AI? And vice versa: what role do AI researchers play in the scientific investigations of computation, artificial intelligence, and intelligence more broadly? Do such roles come with responsibilities and ethical duties? We will explore these questions dialogically (i.e., through dialogue, discussions, and presentations), as well as through lectures. Perspectives will include critical, historical, sociological, and philosophical angles. We will touch on: The history of AI, going as far back as ancient Chinese and Greek civilizations; future projections, including the possibility of another "AI winter." Ultimately, you will aim to answer for yourselves: Is AI a science? Can it be? Will it be? The course will be examined through: Group presentation – You will be split into teams to create a presentation (see Presentation). The presentation combines: Group collaboration, An individual (or more than one person’s) presentation (10 minutes max). Final essay – You may choose a topic related to the course and aligned with your background or interests (see Essay). The essay will take the form of a literature review or journal-style article, aiming to: Deepen and/or broaden your knowledge on one or more topics from the course, Allow you to zoom in or out and develop your own unique perspective. Learning Goals: AI as a Science aims to teach you — and for you to teach yourselves — how to: Define science and AI, critically and in your own words. Understand the historical context of AI: When did AI or AI-like disciplines and ideas emerge? Understand the current state of AI and explore possible futures: Is time a flat circle when it comes to recurring “AI winters”?

Document

Olivia Guest

August 23, 2023

Tutorial: Decision Theory

The learner gets acquainted with non-quantitative and quantitative approaches to ethics students can define and calculate optimal decisions using univariate distributions and utility/loss functions students can know the elements of decision networks: stochastic/action/utility nodes students can know and apply Bayes' rule (multivariate) students can construct a Naive Bayesian Network (NBN) model students can expand NBN to an NBN-based decision network students can calculate the value of perfect information

Website

HCAIM Consortium

June 20, 2024

Testing theory: an introduction

These lecture notes are a follow up on Adjustment theory. Adjustment theory deals with the optimal combination of redundant measurements together with the estimation of unknown parameters. There are two main reasons for performing redundant measurements. First, the wish to increase the accuracy of the results computed. Second, the requirement to be able to check for mistakes or errors. The present book addresses this second topic. Although one always will try one's best to avoid making mistakes, they can and will occasionally happen. It is therefore of importance to have ways of detecting and identifying such mistakes. Mistakes or errors can come in many different guises. They could be caused by mistakes made by the observer, or by the fact that defective instruments are used, or by wrong assumptions about the functional relations between the observables. When passed unnoticed, these errors will deteriorate the final results. The goal of this introductory course on testing theory is therefore to convey the necessary knowledge for testing the validity of both the measurements and the mathematical model. Typical questions that will be addressed are: 'How to check the validity of the mathematical model? How to search for certain mistakes or errors? How well can errors be traced? And how do undetected errors affect the final results?' The theory is worked out in detail for the important case of linear(ized) models. Both the parametric form (observation equations) and the implicit form (condition equations) of linear models are treated. As an additional aid in understanding the basic principles involved, a geometric interpretation is given throughout. Attention is also paid to the performance of the testing procedures. The closely related concept of reliability is introduced and diagnostic measures are given to determine the size of the minimal detectable biases. In this introductory text the methodology of testing is emphasized, although various examples are given to illustrate the theory. The methods discussed form the basis for geodetic quality control and they provide the ingredients for the formulation of guidelines for the reliable design of measurement set-ups.

Open (Access) Text Book

Peter Teunissen

August 23, 2023

Lecture: Theory of Federated Learning (Profiling and Personalization)

Provide the motivations for doing Federated Learning Provide an initial understanding of the basic techniques for Federated Learning Discuss the main limitations and the challenges connected to them

Website

HCAIM Consortium

October 9, 2023

Jupyter Open Textbook: demo book

This demonstration book is meant as an explanation and working example of the possibilities of making an online open textbook with the Jupyter books software. Jupyter books are made for the web. They incorporate many of the features we expect from modern websites, such as adaptive layout (readable on phones, tablets, and large computer screens), internal and external referencing, and adaptable formatting (separation of content and layout). Besides some information on how to work with MarkDown, this book shows examples of different interactive elements, like graphs with sliders, applets, 3D interactive figures, embedded websites and a quiz.

Open (Access) Text Book

Timon Idema